[jira] [Created] (SYSTEMML-1740) Missing call to super in FunctionOp#computeMemEstimate()
Ted Yu created SYSTEMML-1740: Summary: Missing call to super in FunctionOp#computeMemEstimate() Key: SYSTEMML-1740 URL: https://issues.apache.org/jira/browse/SYSTEMML-1740 Project: SystemML Issue Type: Bug Reporter: Ted Yu Priority: Minor super.computeMemEstimate() should be called, as is done in other classes such as AggBinaryOp -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Created] (SYSTEMML-1739) Ineffective null check in MLContextConversionUtil#binaryBlocksToFrameObject()
Ted Yu created SYSTEMML-1739:
Summary: Ineffective null check in
MLContextConversionUtil#binaryBlocksToFrameObject()
Key: SYSTEMML-1739
URL: https://issues.apache.org/jira/browse/SYSTEMML-1739
Project: SystemML
Issue Type: Bug
Reporter: Ted Yu
Priority: Minor
{code}
MatrixCharacteristics mc = (frameMetadata != null) ?
frameMetadata.asMatrixCharacteristics()
: new MatrixCharacteristics();
FrameObject frameObject = new
FrameObject(OptimizerUtils.getUniqueTempFileName(),
new MatrixFormatMetaData(mc, OutputInfo.BinaryBlockOutputInfo,
InputInfo.BinaryBlockInputInfo),
frameMetadata.getFrameSchema().getSchema().toArray(new ValueType[0]));
{code}
null check is performed on frameMetadata.
However, frameMetadata is dereferenced later without check for getFrameSchema().
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[jira] [Created] (SYSTEMML-1738) InputStream should be closed in ScriptFactory#scriptFromInputStream
Ted Yu created SYSTEMML-1738:
Summary: InputStream should be closed in
ScriptFactory#scriptFromInputStream
Key: SYSTEMML-1738
URL: https://issues.apache.org/jira/browse/SYSTEMML-1738
Project: SystemML
Issue Type: Bug
Reporter: Ted Yu
Priority: Minor
{code}
private static Script scriptFromInputStream(InputStream inputStream,
ScriptType scriptType) {
String scriptString = getScriptStringFromInputStream(inputStream);
return scriptFromString(scriptString, scriptType);
{code}
inputStream should be closed after reading is complete.
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[jira] [Created] (SYSTEMML-1737) BufferedReader should be closed in ParameterizedBuiltinCPFileInstruction#createCellResultFile()
Ted Yu created SYSTEMML-1737:
Summary: BufferedReader should be closed in
ParameterizedBuiltinCPFileInstruction#createCellResultFile()
Key: SYSTEMML-1737
URL: https://issues.apache.org/jira/browse/SYSTEMML-1737
Project: SystemML
Issue Type: Bug
Reporter: Ted Yu
Priority: Minor
{code}
BufferedReader fkeyMap = StagingFileUtils.openKeyMap(metaOut);
{code}
BufferedReader should be closed upon exit from method.
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[jira] [Updated] (SYSTEMML-618) Deep Learning DML Library
[
https://issues.apache.org/jira/browse/SYSTEMML-618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-618:
-
Component/s: Algorithms
> Deep Learning DML Library
> -
>
> Key: SYSTEMML-618
> URL: https://issues.apache.org/jira/browse/SYSTEMML-618
> Project: SystemML
> Issue Type: Epic
> Components: Algorithms
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
>
> This issue tracks the creation of a layers-based *deep learning library* in
> pure DML.
> The library contains layers with simple {{forward}} (function evaluation) and
> {{backward}} (gradient computation) functions for affine, convolution (start
> with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.),
> dropout, loss functions, other layers, optimizers, and gradient checks.
> *Examples*: Please see example *scripts* and *notebooks* in the {{examples}}
> folder:
> [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN/examples].
> *SystemML-NN*:
> [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN]
> * Layers:
> ** Core:
> *** Affine
> *** Batch Normalization 1D
> *** Batch Normalization 2D ("Spatial Batch Normalization")
> *** Convolution 2D ("Spatial Convolution")
> *** LSTM
> *** Max Pooling 2D ("Spatial Max Pooling")
> *** RNN
> ** Nonlinearities:
> *** ReLU
> *** Sigmoid
> *** Softmax
> *** Tanh
> ** Loss:
> *** Cross-entropy loss
> *** L1 loss
> *** L2 loss
> *** Log ("Logistic") loss
> ** Regularization:
> *** Dropout
> *** L1 reg
> *** L2 reg
> * Optimizers:
> ** Adagrad
> ** Adam
> ** RMSprop
> ** SGD
> ** SGD w/ Momentum
> ** SGD w/ Nesterov Momentum
> * Tests:
> ** Gradient Checks
> ** Unit Tests
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[jira] [Updated] (SYSTEMML-1679) Add a new threshold utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1679?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1679:
--
Issue Type: New Feature (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Add a new threshold utility function
>
>
> Key: SYSTEMML-1679
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1679
> Project: SystemML
> Issue Type: New Feature
>Reporter: Mike Dusenberry
>Assignee: Fei Hu
> Labels: SYSTEMML-618
> Fix For: SystemML 1.0
>
>
> We should add a new {{threshold}} utility function (in {{nn/util.dml}}) that
> accepts a matrix {{X}} and a threshold parameter {{thresh}} and returns an
> indicator matrix {{out}} with values in \{0, 1\} depending on whether or not
> the values in {{X}} are above {{thresh}}. We could use this, for example,
> for determining the predicted class in a binary classification problem given
> the output of a sigmoid layer.
> We should also add a test case in {{nn/test/test.dml}}.
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[jira] [Updated] (SYSTEMML-1675) Add a new 2D depthwise transpose convolution layer
[ https://issues.apache.org/jira/browse/SYSTEMML-1675?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1675: -- Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add a new 2D depthwise transpose convolution layer > -- > > Key: SYSTEMML-1675 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1675 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 1.0 > > > A depthwise transpose convolution (1) applies a different filter to each > unique group of M input channels separately, thus condensing each group of M > input channels to 1 output channel, and (2) concatenates the results into a > single volume with C/M output channels. This is in contrast to a regular 2D > transpose convolution, in which all of the filters would be applied to all of > the input channels at once. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1674) Add a new 2D depthwise convolution layer
[ https://issues.apache.org/jira/browse/SYSTEMML-1674?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1674: -- Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add a new 2D depthwise convolution layer > > > Key: SYSTEMML-1674 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1674 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 1.0 > > > A depthwise convolution (1) applies a different set of M filters to each > input channel separately, thus expanding each input channel to M output > channels, and (2) concatenates the results into a single volume with C*M > output channels. This is in contrast to a regular 2D convolution, in which > all of the filters would be applied to all of the input channels at once. -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1432) Extend `util::pad_image` with a `pad_value` parameter
[
https://issues.apache.org/jira/browse/SYSTEMML-1432?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1432:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Extend `util::pad_image` with a `pad_value` parameter
> -
>
> Key: SYSTEMML-1432
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1432
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
>Priority: Minor
> Labels: SYSTEMML-618
> Fix For: SystemML 0.14
>
>
> Currently, our {{util::pad_image}} function performs zero-padding, which is
> correct for operations such as convolution. In some cases, such as max
> pooling, it is necessary to pad with a different value such as negative
> infinity. Therefore, we should extend our {{util::pad_image}} function to
> accept a {{pad_value}} parameter with a noted typical value of 0.
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[jira] [Updated] (SYSTEMML-1413) Extract test-only utilities from `nn/util.dml` to new `nn/test/util.dml`
[ https://issues.apache.org/jira/browse/SYSTEMML-1413?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1413: -- Issue Type: Improvement (was: Sub-task) Parent: (was: SYSTEMML-618) > Extract test-only utilities from `nn/util.dml` to new `nn/test/util.dml` > > > Key: SYSTEMML-1413 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1413 > Project: SystemML > Issue Type: Improvement >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.14 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1414) Rename `nn/layers/conv.dml` to `nn/layers/conv2d.dml`
[
https://issues.apache.org/jira/browse/SYSTEMML-1414?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1414:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Rename `nn/layers/conv.dml` to `nn/layers/conv2d.dml`
> -
>
> Key: SYSTEMML-1414
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1414
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
> Fix For: SystemML 1.0
>
>
> Note that this breaks the current API. This is fine though since the {{nn}}
> library is currently in staging.
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[jira] [Updated] (SYSTEMML-1417) Rename `nn/layers/max_pool_builtin.dml` to `nn/layers/max_pool2d_builtin.dml`
[
https://issues.apache.org/jira/browse/SYSTEMML-1417?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1417:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Rename `nn/layers/max_pool_builtin.dml` to `nn/layers/max_pool2d_builtin.dml`
> -
>
> Key: SYSTEMML-1417
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1417
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
>Priority: Minor
> Labels: SYSTEMML-618
> Fix For: SystemML 1.0
>
>
> Note that this breaks the current API. This is fine though since the {{nn}}
> library is currently in staging.
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[jira] [Updated] (SYSTEMML-1409) Add batch normalization layer
[ https://issues.apache.org/jira/browse/SYSTEMML-1409?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1409: -- Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add batch normalization layer > - > > Key: SYSTEMML-1409 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1409 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.14 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1389) Update API: Pass in all outputs from `forward` to `backward` for performance
[
https://issues.apache.org/jira/browse/SYSTEMML-1389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1389:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Update API: Pass in all outputs from `forward` to `backward` for performance
>
>
> Key: SYSTEMML-1389
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1389
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
>
> Currently, we do not pass the outputs of the {{forward}} functions to the
> {{backward}} functions in the {{nn}} library. This aims to update the
> {{backward}} API to include (1) all relevant gradients from upstream, (2)
> *all* outputs from {{forward}}, and (3) *all* inputs given to {{forward}}.
> Effectively, this would be equivalent to having a class object that maintains
> all configuration and input + output tensors. If we had an object oriented
> design, most of the parameters to `forward` and `backward` would just be
> instance variables and thus would be accessible by `forward` or `backward` as
> needed. Given that we don't have that design, this API update mimics that by
> allowing `backward` to have access to any inputs available to `forward`, as
> well as any outputs produced by `forward`. This provides two benefits:
> first, many layers can benefit from a performance perspective from having
> access to the outputs of the {{forward}} function within the {{backward}}
> function, and second, this makes the API much simpler and less error prone by
> allowing for simple copy-and-paste of forward inputs and outputs as arguments
> to {{backward}} and by removing ambiguity related to the parameters. A
> downside is that often times, not every single parameter is needed by
> {{backward}}.
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[jira] [Updated] (SYSTEMML-1408) Add padding parameters to max-pooling layers
[
https://issues.apache.org/jira/browse/SYSTEMML-1408?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1408:
--
Issue Type: New Feature (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Add padding parameters to max-pooling layers
>
>
> Key: SYSTEMML-1408
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1408
> Project: SystemML
> Issue Type: New Feature
> Components: Algorithms
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
> Fix For: SystemML 0.14
>
>
> Currently, the {{max_pool}} layers do not accept padding arguments. To
> provide more flexibility, we should add padding parameters to all of the max
> pooling layers, and update the tests accordingly.
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[jira] [Updated] (SYSTEMML-1384) Revisit the weight and bias of fully connected layer
[ https://issues.apache.org/jira/browse/SYSTEMML-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1384: -- Issue Type: Task (was: Sub-task) Parent: (was: SYSTEMML-618) > Revisit the weight and bias of fully connected layer > > > Key: SYSTEMML-1384 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1384 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > Labels: SYSTEMML-618 > > Since all our bias are column vector (which is consistent with Keras/Caffe), > whereas bias of fully connected layer is a row-vector. Similarly, the weight > that is passed to caffe is transpose of weights passed to SystemML (since > both store in row-major NCHW format). > Making the dimensions consistent will simplify loading of Caffe/Keras models. > [~mwdus...@us.ibm.com] -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1115) Vectorize Convolution
[
https://issues.apache.org/jira/browse/SYSTEMML-1115?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1115:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Vectorize Convolution
> -
>
> Key: SYSTEMML-1115
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1115
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
>
> Once {{im2col}} and {{pad_image}} are vectorized over all examples, the
> convolution layer functions can be vectorized as well. This would result in
> a single matrix multiply in the forward function that computes the
> convolution over all {{n}} images at once, rather than using a {{parfor}}
> loop over the examples.
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[jira] [Updated] (SYSTEMML-1114) Vectorize pad_image
[
https://issues.apache.org/jira/browse/SYSTEMML-1114?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1114:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Vectorize pad_image
> ---
>
> Key: SYSTEMML-1114
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1114
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
>
> The goal here is to vectorize the {{pad_image}} function over all examples
> using built-in operations. By vectorizing over all examples, the convolution
> layer functions can be vectorized as well.
> https://issues.apache.org/jira/browse/SYSTEMML-633?focusedCommentId=15244030=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15244030
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[jira] [Updated] (SYSTEMML-1113) Vectorize im2col
[
https://issues.apache.org/jira/browse/SYSTEMML-1113?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-1113:
--
Issue Type: Improvement (was: Sub-task)
Parent: (was: SYSTEMML-618)
> Vectorize im2col
>
>
> Key: SYSTEMML-1113
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1113
> Project: SystemML
> Issue Type: Improvement
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
> Labels: SYSTEMML-618
>
> The goal here is to vectorize the {{im2col}} function over all examples using
> built-in operations. By vectorizing over all examples, the convolution layer
> functions can be vectorized as well.
> https://issues.apache.org/jira/browse/SYSTEMML-633?focusedCommentId=15244065=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15244065
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[jira] [Updated] (SYSTEMML-1383) Performance testing of individual layer for common data shapes
[ https://issues.apache.org/jira/browse/SYSTEMML-1383?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1383: -- Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Performance testing of individual layer for common data shapes > -- > > Key: SYSTEMML-1383 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1383 > Project: SystemML > Issue Type: New Feature >Reporter: Niketan Pansare > Labels: SYSTEMML-618 > > After performance testing, we can move the NN library from staging to utils > or algorithms. Then we can extend source() method to read the nn > layers/optimizer directly from jar as resource similar to mllearn algorithms. > [~reinwald] [~mboehm7] [~mwdus...@us.ibm.com] -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-908) Improve Test Suite
[ https://issues.apache.org/jira/browse/SYSTEMML-908?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-908: - Issue Type: Improvement (was: Sub-task) Parent: (was: SYSTEMML-618) > Improve Test Suite > -- > > Key: SYSTEMML-908 > URL: https://issues.apache.org/jira/browse/SYSTEMML-908 > Project: SystemML > Issue Type: Improvement >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry >Priority: Minor > Labels: SYSTEMML-618 > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-904) Update the API calls in the deep learning notebook examples.
[ https://issues.apache.org/jira/browse/SYSTEMML-904?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-904: - Issue Type: Improvement (was: Sub-task) Parent: (was: SYSTEMML-618) > Update the API calls in the deep learning notebook examples. > > > Key: SYSTEMML-904 > URL: https://issues.apache.org/jira/browse/SYSTEMML-904 > Project: SystemML > Issue Type: Improvement >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry >Priority: Minor > Labels: SYSTEMML-618 > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-799) Add Gradient Check Testing to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-799?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-799: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Gradient Check Testing to DML Deep Learning Library > --- > > Key: SYSTEMML-799 > URL: https://issues.apache.org/jira/browse/SYSTEMML-799 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-808) Add LSTM layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-808?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-808: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add LSTM layer to DML Deep Learning Library > --- > > Key: SYSTEMML-808 > URL: https://issues.apache.org/jira/browse/SYSTEMML-808 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-802) Add MNIST Softmax Classifier Example to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-802?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-802: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add MNIST Softmax Classifier Example to DML Deep Learning Library > - > > Key: SYSTEMML-802 > URL: https://issues.apache.org/jira/browse/SYSTEMML-802 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-801) Add Builtin Max Pooling layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-801?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-801: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Builtin Max Pooling layer to DML Deep Learning Library > -- > > Key: SYSTEMML-801 > URL: https://issues.apache.org/jira/browse/SYSTEMML-801 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-803) Add MNIST LeNet Example to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-803: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add MNIST LeNet Example to DML Deep Learning Library > > > Key: SYSTEMML-803 > URL: https://issues.apache.org/jira/browse/SYSTEMML-803 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-800) Add Builtin Convolution layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-800?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-800: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Builtin Convolution layer to DML Deep Learning Library > -- > > Key: SYSTEMML-800 > URL: https://issues.apache.org/jira/browse/SYSTEMML-800 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-792) Add L2 Regularization layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-792?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-792: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add L2 Regularization layer to DML Deep Learning Library > > > Key: SYSTEMML-792 > URL: https://issues.apache.org/jira/browse/SYSTEMML-792 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-798) Add SGD w/ Nesterov Momentum Optimizer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-798?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-798: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add SGD w/ Nesterov Momentum Optimizer to DML Deep Learning Library > --- > > Key: SYSTEMML-798 > URL: https://issues.apache.org/jira/browse/SYSTEMML-798 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-795) Add Adam Optimizer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-795?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-795: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Adam Optimizer to DML Deep Learning Library > --- > > Key: SYSTEMML-795 > URL: https://issues.apache.org/jira/browse/SYSTEMML-795 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-793) Add Adagrad Optimizer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-793?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-793: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Adagrad Optimizer to DML Deep Learning Library > -- > > Key: SYSTEMML-793 > URL: https://issues.apache.org/jira/browse/SYSTEMML-793 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-791) Add L1 Regularization layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-791?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-791: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add L1 Regularization layer to DML Deep Learning Library > > > Key: SYSTEMML-791 > URL: https://issues.apache.org/jira/browse/SYSTEMML-791 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-783) Add Sigmoid nonlinearity layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-783?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-783: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Sigmoid nonlinearity layer to DML Deep Learning Library > --- > > Key: SYSTEMML-783 > URL: https://issues.apache.org/jira/browse/SYSTEMML-783 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-787) Add L1 Loss layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-787?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-787: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add L1 Loss layer to DML Deep Learning Library > -- > > Key: SYSTEMML-787 > URL: https://issues.apache.org/jira/browse/SYSTEMML-787 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-788) Add L2 Loss layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-788?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-788: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add L2 Loss layer to DML Deep Learning Library > -- > > Key: SYSTEMML-788 > URL: https://issues.apache.org/jira/browse/SYSTEMML-788 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-782) Add ReLU nonlinearity layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-782?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-782: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add ReLU nonlinearity layer to DML Deep Learning Library > > > Key: SYSTEMML-782 > URL: https://issues.apache.org/jira/browse/SYSTEMML-782 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-784) Add Softmax nonlinearity layer to DML Deep Learning Library
[ https://issues.apache.org/jira/browse/SYSTEMML-784?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-784: - Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add Softmax nonlinearity layer to DML Deep Learning Library > --- > > Key: SYSTEMML-784 > URL: https://issues.apache.org/jira/browse/SYSTEMML-784 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > Fix For: SystemML 0.10 > > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-1680) Add a new max margin loss
[ https://issues.apache.org/jira/browse/SYSTEMML-1680?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mike Dusenberry updated SYSTEMML-1680: -- Issue Type: New Feature (was: Sub-task) Parent: (was: SYSTEMML-618) > Add a new max margin loss > - > > Key: SYSTEMML-1680 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1680 > Project: SystemML > Issue Type: New Feature >Reporter: Mike Dusenberry >Assignee: Mike Dusenberry > Labels: SYSTEMML-618 > -- This message was sent by Atlassian JIRA (v6.4.14#64029)
[jira] [Updated] (SYSTEMML-618) Deep Learning DML Library
[
https://issues.apache.org/jira/browse/SYSTEMML-618?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry updated SYSTEMML-618:
-
Issue Type: Epic (was: New Feature)
> Deep Learning DML Library
> -
>
> Key: SYSTEMML-618
> URL: https://issues.apache.org/jira/browse/SYSTEMML-618
> Project: SystemML
> Issue Type: Epic
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
>
> This issue tracks the creation of a layers-based *deep learning library* in
> pure DML.
> The library contains layers with simple {{forward}} (function evaluation) and
> {{backward}} (gradient computation) functions for affine, convolution (start
> with 2D), max-pooling, non-linearities (relu, sigmoid, softmax, etc.),
> dropout, loss functions, other layers, optimizers, and gradient checks.
> *Examples*: Please see example *scripts* and *notebooks* in the {{examples}}
> folder:
> [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN/examples].
> *SystemML-NN*:
> [https://github.com/apache/incubator-systemml/tree/master/scripts/staging/SystemML-NN]
> * Layers:
> ** Core:
> *** Affine
> *** Batch Normalization 1D
> *** Batch Normalization 2D ("Spatial Batch Normalization")
> *** Convolution 2D ("Spatial Convolution")
> *** LSTM
> *** Max Pooling 2D ("Spatial Max Pooling")
> *** RNN
> ** Nonlinearities:
> *** ReLU
> *** Sigmoid
> *** Softmax
> *** Tanh
> ** Loss:
> *** Cross-entropy loss
> *** L1 loss
> *** L2 loss
> *** Log ("Logistic") loss
> ** Regularization:
> *** Dropout
> *** L1 reg
> *** L2 reg
> * Optimizers:
> ** Adagrad
> ** Adam
> ** RMSprop
> ** SGD
> ** SGD w/ Momentum
> ** SGD w/ Nesterov Momentum
> * Tests:
> ** Gradient Checks
> ** Unit Tests
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[jira] [Closed] (SYSTEMML-1736) Add new 2D top_k utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry closed SYSTEMML-1736.
-
> Add new 2D top_k utility function
> -
>
> Key: SYSTEMML-1736
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1736
> Project: SystemML
> Issue Type: Sub-task
>Reporter: Mike Dusenberry
>Assignee: Fei Hu
> Fix For: SystemML 1.0
>
>
> We should add a new {{top_k2d}} utility function (in {{nn/util.dml}}) that
> accepts a matrix {{X}} and return matrices {{values}} and {{indices}} with
> the top {{k}} values (i.e. probabilities) and associated indices (i.e.
> classes) along a certain dimension. This will be modeled after the
> [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k]. For the 2D case,
> {{top_k}} will operate on the channels dimension. A typical use case here is
> that in which {{X}} is the output of a {{softmax2d}} layer (so each channel
> contains a set of normalized class probabilities), and {{values}} and
> {{indices}} will contain the top {{k}} probabilities and indices along the
> channel axis. This scenario would be common in an image segmentation
> problem, in which every pixel of the output image will have a set of class
> probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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[jira] [Assigned] (SYSTEMML-1736) Add new 2D top_k utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry reassigned SYSTEMML-1736:
-
Assignee: Mike Dusenberry (was: Fei Hu)
> Add new 2D top_k utility function
> -
>
> Key: SYSTEMML-1736
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1736
> Project: SystemML
> Issue Type: Sub-task
>Reporter: Mike Dusenberry
>Assignee: Mike Dusenberry
>
> We should add a new {{top_k2d}} utility function (in {{nn/util.dml}}) that
> accepts a matrix {{X}} and return matrices {{values}} and {{indices}} with
> the top {{k}} values (i.e. probabilities) and associated indices (i.e.
> classes) along a certain dimension. This will be modeled after the
> [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k]. For the 2D case,
> {{top_k}} will operate on the channels dimension. A typical use case here is
> that in which {{X}} is the output of a {{softmax2d}} layer (so each channel
> contains a set of normalized class probabilities), and {{values}} and
> {{indices}} will contain the top {{k}} probabilities and indices along the
> channel axis. This scenario would be common in an image segmentation
> problem, in which every pixel of the output image will have a set of class
> probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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[jira] [Assigned] (SYSTEMML-1736) Add new 2D top_k utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry reassigned SYSTEMML-1736:
-
Assignee: Fei Hu (was: Mike Dusenberry)
> Add new 2D top_k utility function
> -
>
> Key: SYSTEMML-1736
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1736
> Project: SystemML
> Issue Type: Sub-task
>Reporter: Mike Dusenberry
>Assignee: Fei Hu
>
> We should add a new {{top_k2d}} utility function (in {{nn/util.dml}}) that
> accepts a matrix {{X}} and return matrices {{values}} and {{indices}} with
> the top {{k}} values (i.e. probabilities) and associated indices (i.e.
> classes) along a certain dimension. This will be modeled after the
> [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k]. For the 2D case,
> {{top_k}} will operate on the channels dimension. A typical use case here is
> that in which {{X}} is the output of a {{softmax2d}} layer (so each channel
> contains a set of normalized class probabilities), and {{values}} and
> {{indices}} will contain the top {{k}} probabilities and indices along the
> channel axis. This scenario would be common in an image segmentation
> problem, in which every pixel of the output image will have a set of class
> probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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[jira] [Closed] (SYSTEMML-1678) Add new 1D top_k utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry closed SYSTEMML-1678.
-
> Add new 1D top_k utility function
> -
>
> Key: SYSTEMML-1678
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1678
> Project: SystemML
> Issue Type: Sub-task
>Reporter: Mike Dusenberry
>Assignee: Fei Hu
> Fix For: SystemML 1.0
>
>
> We should add new {{top_k}} and {{top_k2d}} utility functions (in
> {{nn/util.dml}}) that accept a matrix {{X}} and return matrices {{values}}
> and {{indices}} with the top {{k}} values (i.e. probabilities) and associated
> indices (i.e. classes) along a certain dimension. This will be modeled after
> the [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k] For the 1D case,
> {{top_k}} will operate on the columns dimension. A typical use case is that
> in which {{X}} is the output of a {{softmax}} layer (so each row contains a
> set of normalized class probabilities), and {{values}} and {{indices}} will
> contain rows with the top {{k}} probabilities and class indices as described
> above. For the 2D case, {{top_k}} will operate on the channels dimension. A
> typical use case here is that in which {{X}} is the output of a {{softmax2d}}
> layer (so each channel contains a set of normalized class probabilities), and
> {{values}} and {{indices}} will contain the top {{k}} probabilities and
> indices along the channel axis. This scenario would be common in an image
> segmentation problem, in which every pixel of the output image will have a
> set of class probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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[jira] [Resolved] (SYSTEMML-1678) Add new 1D top_k utility function
[
https://issues.apache.org/jira/browse/SYSTEMML-1678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mike Dusenberry resolved SYSTEMML-1678.
---
Resolution: Fixed
Fix Version/s: SystemML 1.0
Merged in [commit 2e78eb9 |
https://github.com/apache/systemml/commit/2e78eb9a56148f9e27ea1cb646e2d6dd528d251e].
> Add new 1D top_k utility function
> -
>
> Key: SYSTEMML-1678
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1678
> Project: SystemML
> Issue Type: Sub-task
>Reporter: Mike Dusenberry
>Assignee: Fei Hu
> Fix For: SystemML 1.0
>
>
> We should add new {{top_k}} and {{top_k2d}} utility functions (in
> {{nn/util.dml}}) that accept a matrix {{X}} and return matrices {{values}}
> and {{indices}} with the top {{k}} values (i.e. probabilities) and associated
> indices (i.e. classes) along a certain dimension. This will be modeled after
> the [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k] For the 1D case,
> {{top_k}} will operate on the columns dimension. A typical use case is that
> in which {{X}} is the output of a {{softmax}} layer (so each row contains a
> set of normalized class probabilities), and {{values}} and {{indices}} will
> contain rows with the top {{k}} probabilities and class indices as described
> above. For the 2D case, {{top_k}} will operate on the channels dimension. A
> typical use case here is that in which {{X}} is the output of a {{softmax2d}}
> layer (so each channel contains a set of normalized class probabilities), and
> {{values}} and {{indices}} will contain the top {{k}} probabilities and
> indices along the channel axis. This scenario would be common in an image
> segmentation problem, in which every pixel of the output image will have a
> set of class probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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